

import argparse
import logging
import os

import datasets
import tensorflow as tf

from transformers import AutoTokenizer


__snake_case				:      Any   		=			logging.getLogger(__name__)


def 					_UpperCAmelCase				(						):

			'''simple docstring'''



			a_						:						Any 							= argparse.ArgumentParser(
			    description="""Prepare TFRecord shards from pre-tokenized samples of the wikitext dataset.""")
			parser.add_argument(
			    """--dataset_name"""  ,	type=a_  ,	default="""wikitext"""  ,	help="""Name of the training. Explore datasets at: hf.co/datasets."""  ,	)
			parser.add_argument(
			    """--dataset_config"""  ,	type=a_  ,	default="""wikitext-103-raw-v1"""  ,	help="""Configuration name of the dataset.""")
			parser.add_argument(
			    """--tokenizer_name_or_path"""  ,	type=a_  ,	default="""sayakpaul/unigram-tokenizer-wikitext"""  ,	help="""Tokenizer identifier. Can be a local filepath or a Hub identifier."""  ,	)
			parser.add_argument(
			    """--shard_size"""  ,	type=a_  ,	default=1_0_0_0  ,	help="""Number of entries to go in a single shard."""  ,	)
			parser.add_argument("""--split"""  ,	type=a_  ,	default="""train"""  ,	choices=["""train""", """test""", """validation"""])
			parser.add_argument(
			    """--limit"""  ,	default=a_  ,	type=a_  ,	help="""Limit the number of shards (used for debugging)."""  ,	)
			parser.add_argument(
			    """--max_length"""  ,	type=a_  ,	default=5_1_2  ,	help="""Maximum sequence length. For training on TPUs, it helps to have a maximum"""
			    """ sequence length that is a multiple of 8."""  ,	)
			parser.add_argument(
			    """--output_dir"""  ,	default="""tf-tpu"""  ,	type=a_  ,	help="""Output directory where the TFRecord shards will be saved. If the"""
			    """ path is appended with `gs://` ('gs://tf-tpu', for example) then the TFRecord"""
			    """ shards will be directly saved to a Google Cloud Storage bucket."""  ,	)

			a_						:						List[Any] 							= parser.parse_args()
			return args


def 					_UpperCAmelCase				(						a__):

			'''simple docstring'''



			def fn(a__):
						return tokenizer(examples["""text"""])

			return fn


def 					_UpperCAmelCase				(						a__):

			'''simple docstring'''



			a_						:						Union[str, Any] 							= []
			for i in range(len(tokenized_data["""input_ids"""])):
						a_						:						str 							= {
						    "input_ids": tf.train.Feature(intaa_list=tf.train.IntaaList(value=tokenized_data["""input_ids"""][i])),
						    "attention_mask": tf.train.Feature(
						        intaa_list=tf.train.IntaaList(value=tokenized_data["""attention_mask"""][i])),
						}
						a_						:						List[str] 							= tf.train.Features(feature=a_)
						a_						:						Tuple 							= tf.train.Example(features=a_)
						a_						:						List[Any] 							= example.SerializeToString()
						records.append(a_)
			return records


def 					_UpperCAmelCase				(						a__):

			'''simple docstring'''



			a_						:						Dict 							= datasets.load_dataset(args.dataset_name  ,	args.dataset_config  ,	split=args.split)

			if args.limit is not None:
						a_						:						Dict 							= min(len(a_)  ,	args.limit)
						a_						:						Tuple 							= dataset.select(range(a_))
						print(f'''Limiting the dataset to {args.limit} entries.''')

			a_						:						Optional[Any] 							= AutoTokenizer.from_pretrained(args.tokenizer_name_or_path)

			# Handle output directory creation.
			# For serializing into a Google Cloud Storage Bucket, one needs to first
			# create a bucket.
			if "gs" not in args.output_dir:
						if not os.path.exists(args.output_dir):
									os.makedirs(args.output_dir)
						a_						:						List[Any] 							= os.path.join(args.output_dir  ,	args.split)
						if not os.path.exists(a_):
									os.makedirs(a_)
			else:
						a_						:						Tuple 							= os.path.join(args.output_dir  ,	args.split)

			# Tokenize the whole dataset at once.
			a_						:						int 							= tokenize_function(a_)
			a_						:						Tuple 							= dataset.map(a_  ,	batched=a_  ,	num_proc=4  ,	remove_columns=["""text"""])

			# We need to concatenate all our texts together, and then split the result
			# into chunks of a fixed size, which we will call block_size. To do this, we
			# will use the map method again, with the option batched=True. When we use batched=True,
			# the function we pass to map() will be passed multiple inputs at once, allowing us
			# to group them into more or fewer examples than we had in the input.
			# This allows us to create our new fixed-length samples. The advantage of this
			# method is that we don't lose a whole lot of content from the dataset compared to the
			# case where we simply tokenize with a pre-defined max_length.

			def group_texts(a__):
						# Concatenate all texts.
						a_						:						str 							= {k: sum(examples[k]  ,	[]) for k in examples.keys()}
						a_						:						List[str] 							= len(concatenated_examples[list(examples.keys())[0]])
						# We drop the small remainder, though you could add padding instead if the model supports it
						# In this, as in all things, we advise you to follow your heart 🫀
						a_						:						Tuple 							= (total_length // args.max_length) * args.max_length
						# Split by chunks of max_len.
						a_						:						int 							= {
						    k: [t[i : i + args.max_length] for i in range(0  ,	a_  ,	args.max_length)]
						    for k, t in concatenated_examples.items()
						}
						return result

			a_						:						Dict 							= dataset_tokenized.map(a_  ,	batched=a_  ,	batch_size=1_0_0_0  ,	num_proc=4)

			a_						:						Tuple 							= 0
			a_						:						Any 							= 0
			for shard in range(0  ,	len(a_)  ,	args.shard_size):
						a_						:						Optional[int] 							= grouped_dataset[shard : shard + args.shard_size]
						a_						:						Tuple 							= len(dataset_snapshot["""input_ids"""])
						a_						:						Optional[int] 							= os.path.join(a_  ,	f'''dataset-{shard_count}-{records_containing}.tfrecord''')
						a_						:						Dict 							= get_serialized_examples(a_)

						with tf.io.TFRecordWriter(a_) as out_file:
									for i in range(len(a_)):
												a_						:						Any 							= serialized_examples[i]
												out_file.write(a_)
									print("""Wrote file {} containing {} records""".format(a_  ,	a_))

						shard_count += 1
						total_records += records_containing

			with open(f'''split-{args.split}-records-count.txt'''  ,	"""w""") as f:
						print(f'''Total {args.split} records: {total_records}'''  ,	file=a_)


if __name__ == "__main__":
	__snake_case				:      Any   		=			parse_args()
	main(args)









'''simple docstring'''






from __future__ import annotations

lowerCAmelCase									=   []






def   __A    (				a_    :	list[list[int]]   ,a_    :	int   ,a_    :	int						):
		for i in range(len(a_						)						):
				if board[row][i] == 1:
						return False
		for i in range(len(a_						)						):
				if board[i][column] == 1:
						return False
		for i, j in zip(range(a_   ,-1   ,-1						)   ,range(a_   ,-1   ,-1						)						):
				if board[i][j] == 1:
						return False
		for i, j in zip(range(a_   ,-1   ,-1						)   ,range(a_   ,len(a_						)						)						):
				if board[i][j] == 1:
						return False
		return True






def   __A    (				a_    :	list[list[int]]   ,a_    :	int						):
		if row >= len(a_						):
				solution.append(a_						)
				printboard(a_						)
				print()
				return True
		for i in range(len(a_						)						):
				if is_safe(a_   ,a_   ,a_						):
						lowerCAmelCase  :     Dict       						= 1
						solve(a_   ,row + 1						)
						lowerCAmelCase  :     Optional[int]       						= 0
		return False






def   __A    (				a_    :	list[list[int]]						):
		for i in range(len(a_						)						):
				for j in range(len(a_						)						):
						if board[i][j] == 1:
								print("Q"   ,end=" "						)
						else:
								print("."   ,end=" "						)
				print()


# n=int(input("The no. of queens"))
lowerCAmelCase									=   8
lowerCAmelCase									=   [[0 for i in range(n)] for j in range(n)]
solve(board, 0)

print("""The total no. of solutions are :""", len(solution))



